Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups

TL;DR

This paper introduces a model-free, efficient method for estimating heterogeneous treatment effects across multiple subgroups, improving accuracy and robustness over traditional parametric approaches, with applications in personalized medicine.

Contribution

It develops a semiparametric, one-step TMLE framework for simultaneous treatment effect estimation in multiple subgroups, including a robust variation for small propensity scores.

Findings

Demonstrates substantial finite sample improvements over conventional methods.

Unveils potential treatment effect heterogeneity of rs12916-T allele in reducing Alzheimer's risk.

Validates the method through simulations and a real case study.

Abstract

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this manuscript, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.